A lot of companies are failing to do this really well. Even well tested systems can have problems because during testing they give the right answer but for the wrong reasons. So it's, you shouldn't think of testing and explainability as a, you know, start of the process. It has to be continuous through production and while the models liveBecause otherwise things just go wrong quickly.
To trust something, you need to understand it. And, to understand something, someone often has to explain it. When it comes to AI, explainability can be a real challenge (definitionally, a "black box" is unexplainable)! With AI getting new levels of press and prominence thanks to the explosion of generative AI platforms, the need for explainability continues to grow. But, it's just as important in more conventional situations. Dr. Janet Bastiman, the Chief Data Scientist at Napier, joined Moe and Tim to, well, explain the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.